AI in Life Sciences: Stalled Before It Starts?
Across the life sciences industry, the excitement around generative AI (GenAI) is palpable. Executive teams are greenlighting AI initiatives with urgency—aiming to transform research pipelines, clinical trials, and operational efficiency. Yet on the ground, many of these projects are struggling to gain traction. Despite bold strategic visions, frontline teams report that progress is slow, fragmented, or in some cases, completely stalled.
What’s causing the disconnect?
The Enthusiasm Gap Between Vision and Execution
Leaders see AI as a competitive imperative. According to a 2024 Deloitte report, 83% of life sciences executives say AI will be critical to their organization's success within the next two years. Yet only 29% feel “very prepared” to implement it at scale (1).
That gap is rooted in a growing divide between strategic ambition and operational readiness. While leadership may fast-track budgets and roadmaps, execution teams often face entrenched barriers that undermine momentum.
1. Siloed and Unstructured Data
Life sciences companies generate massive volumes of data—from discovery and preclinical stages to post-market surveillance. But data remains fragmented across R&D, clinical, regulatory, and commercial functions. One study found that up to 80% of life sciences data is unstructured, residing in documents, PDFs, or legacy systems (2).
Without access to harmonized, high-quality data, GenAI models can’t deliver reliable outputs. And cleaning, structuring, and governing this data remains a herculean task for many organizations.
2. Regulatory Ambiguity
GenAI tools raise complex questions around compliance, patient safety, and explainability. Regulatory frameworks are evolving, but guidance specific to GenAI remains limited. In April 2024, the FDA issued a discussion paper on AI in drug development—but formal guidelines are still under development (3).
In the absence of clear standards, many teams default to risk aversion—delaying or limiting AI experimentation due to fears of non-compliance or reputational risk.
3. Talent and Capability Gaps
While AI strategy might be led by central innovation or IT teams, domain experts in clinical and regulatory functions often lack the tools, training, or trust needed to deploy GenAI in their workflows. In PwC’s 2024 AI in Pharma survey, over 60% of frontline workers reported low confidence in applying GenAI tools without significant upskilling (4).
This skills gap makes it difficult to scale pilot programs or transition them into production environments.
Innovation Bottlenecks: Where It Hurts Most
The result of these barriers? Innovation bottlenecks across critical domains:
In R&D, AI-supported target discovery and molecule generation remain underutilized due to disjointed data and model validation hurdles.
In clinical operations, the promise of AI-enhanced protocol design and patient recruitment is slowed by regulatory uncertainty and integration issues.
In digital transformation, disconnected systems and conservative risk cultures keep AI tools in pilot purgatory.
What Leading Teams Are Doing Differently
While many organizations stall, a few are accelerating. Their edge lies not just in AI investment, but in execution discipline:
Data Foundations First: Top-performing companies are investing heavily in data engineering—cleaning, curating, and standardizing data across silos to create AI-ready environments (5).
Cross-Functional Collaboration: Instead of isolating AI in IT or innovation teams, leaders are embedding GenAI into multidisciplinary squads with clinical, regulatory, and commercial input.
Regulatory Co-Creation: Some firms are engaging proactively with regulators, participating in pilot programs or sandboxes to align GenAI initiatives with evolving standards.
Scaled Training and Enablement: Leading organizations are upskilling not just data scientists, but also clinicians, researchers, and regulatory teams—ensuring adoption is broad-based and sustainable.
Conclusion: Closing the Execution Gap
The GenAI wave in life sciences is real—but ambition alone won't deliver transformation. The organizations that succeed won’t just be the ones with the most sophisticated models, but the ones that address the unglamorous but essential execution barriers: data readiness, compliance clarity, and change enablement.
As one industry leader recently put it: “We don't need another AI roadmap—we need a better map of the terrain.”
Footnotes
Deloitte. (2024). AI and the Future of Life Sciences. ↩
IDC Health Insights. (2023). State of Data in Life Sciences. ↩
FDA. (April 2024). Discussion Paper: Artificial Intelligence in Drug Development. ↩
PwC. (2024). AI in Pharma: Preparing the Workforce for GenAI. ↩
McKinsey & Company. (2023). Unlocking AI’s Potential in Biopharma. ↩
